The disclosed embodiments are directed toward autonomous vehicles and, in particular, to systems and methods for identifying and tracking suspect vehicles using a fleet of autonomous vehicles.
Current, non-technical solutions for identifying and tracking suspect vehicles generally rely on human cooperation and tactics. For example, law enforcement receives identifications of suspect vehicles (e.g., license plate numbers, makes and models of vehicles, etc.) and manually attempt to locate vehicles based on imperfect information such as witness testimony.
Current technical solutions attempt to supplement previous pre-computing techniques by supplementing law enforcement with a variety of data. For example, automated toll plazas are configured to capture images of license plates as these vehicles pass through automated toll plazas. Additionally, closed-circuit television (CCTV) may be utilized to inspect areas where suspect vehicles are reported. Law enforcement frequently combines these two technologies to reduce the amount of manpower and man hours needed to locate and detain a suspect vehicle.
These systems notably are incomplete. Specifically, the systems do not fully close the loop of providing an entirely automated and computerized system. Specifically, the use of existing technology to roughly identify vehicles may often produce unreliable results due to limitations of, for example, toll plaza-based imaging systems. Further, these systems may be thwarted by suspects by simply not using, for example, toll plazas. Further, the use of CCTV necessarily relies on an initial location of a suspect vehicle and additionally relies on human operators to inspect images to identify vehicles. These deficiencies result in, as an example, the average car theft recovery rate being below fifty percent in the United States on average.
Thus, there exists a need to improve existing systems and methods of identifying and tracking suspect vehicles.